A conventional approach to threat assessment in vehicle collision warning/avoidance systems is to predict the forward travel path of the host vehicle based on parameters such as speed and yaw rate and to determine if a detected object is in the lane occupied by the host vehicle or a different lane. However, the predicted forward travel path does not necessarily provide a reliable indication of the forward road/lane model due to measurement uncertainty and other reasons such as host and target vehicle maneuvering, and there is no convenient way to assess the confidence of the same-lane/different-lane determination.
The U.S. Pat. No. 7,034,742 to Cong et al. describes another approach in which different possible trajectories of a detected object corresponding to the various lanes of the roadway are modeled with different constraint equations, assuming that the host vehicle is tracking its lane. The degree of agreement between the modeled trajectories and successive measurements of the object position is evaluated to assess the validity or degree-of-belief of the various constraint equations. And the validity or degree-of-belief for a given constraint equation reflects the probability that the detected object is traveling in the respective lane. While the objective of providing a probabilistic assessment of lane assignment is desirable, the approach described by Cong et al. has significant drawbacks and limitations. For example, the assumption that the host vehicle is tracking its lane may be inaccurate, and the various measurements and assumptions about the roadway geometry all have a degree of uncertainty that is not taken into account. Furthermore, the requirement of evaluating the constraint equations over a series of successive measurements results in an undesirable delay between detection of an object and its assignment to a particular lane.
Accordingly, what is needed is an improved method of determining a probabilistic assessment of lane assignment for a detected object that takes into account movement of the host vehicle in its lane and uncertainty in the measured and assumed parameters, and that provides substantially immediate probabilistic lane assignment.